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» Approximation Methods for Supervised Learning
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ESANN
2006
13 years 11 months ago
Reducing policy degradation in neuro-dynamic programming
We focus on neuro-dynamic programming methods to learn state-action value functions and outline some of the inherent problems to be faced, when performing reinforcement learning in...
Thomas Gabel, Martin Riedmiller
DAGM
2006
Springer
14 years 1 months ago
Towards Unsupervised Discovery of Visual Categories
Recently, many approaches have been proposed for visual object category detection. They vary greatly in terms of how much supervision is needed. High performance object detection m...
Mario Fritz, Bernt Schiele
ICML
2009
IEEE
14 years 10 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
PAKDD
2011
ACM
473views Data Mining» more  PAKDD 2011»
13 years 3 months ago
 Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning
Discovering rare categories and classifying new instances of them is an important data mining issue in many fields, but fully supervised learning of a rare class classifier is pr...
Timothy Hospedales, Shaogang Gong and Tao Xiang
NIPS
2008
13 years 11 months ago
Regularized Learning with Networks of Features
For many supervised learning problems, we possess prior knowledge about which features yield similar information about the target variable. In predicting the topic of a document, ...
Ted Sandler, John Blitzer, Partha Pratim Talukdar,...